Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Linear Approximation in Frequency Domain

Linear Approximation in Frequency Domain

Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear.

Determination of Expected Frequency

Determination of Expected Frequency

Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...

Linear Approximation in Time Domain

Linear Approximation in Time Domain

Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length, the...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Biochar-Based Single-Atom Cobalt Catalyst for Efficient Thermal Decomposition of Ammonium Perchlorate: Preparation, Performance and Mechanism.

International journal of molecular sciences·2026

Same author

Leveraging the Oryza telomere-to-telomere genome and wild-rice substitution lines for rice-quality improvement.

Current biology : CB·2026

Same author

Ti Substitution-Induced Inter-Site Distance Effect (ISDE) in Hierarchical Porous Cobalt Titanium Oxide Promoting Selective NH<sub>3</sub> Oxidation for Enhanced Energy Release.

ACS applied materials & interfaces·2026

Same author

SCD1 Regulates Osteoclast Differentiation by Modulating Arachidonic Acid Metabolism and Mitochondrial Homeostasis.

FASEB journal : official publication of the Federation of American Societies for Experimental Biology·2026

Same author

MMGS: a novel genomic prediction framework to integrate genotype, environment and their interactions for multi-environment breeding trials.

Horticulture research·2026

Same author

Partial Delignification of Bamboo for Fabricating High-Performance Porous Carbon Electrodes in Supercapacitors.

Langmuir : the ACS journal of surfaces and colloids·2026

Search research articles

Related Experiment Videos

AFD-Net: a robust exchange rate forecasting framework integrating frequency-domain decomposition and attention

ZeZhong Pan¹, ZeYu Zheng²

¹School of Information Engineering, Huzhou University, No. 759, East 2nd Ring Road, Wuxing District, Huzhou, 313000, Zhejiang, China.

Scientific Reports

|July 14, 2026

Summary

A new deep learning model, AFD-Net (Attention-Enhanced FAN with DLinear), improves exchange rate forecasting by integrating frequency decomposition and attention mechanisms. This novel approach enhances accuracy, particularly for long-term predictions in financial time series analysis.

Keywords:

Attention mechanism DLinear Deep learning models Exchange rate forecasting Feature-wise adaptive normalization (FAN)Frequency-domain decomposition Time series analysis

Related Experiment Videos

Area of Science:

Quantitative Finance
Machine Learning
Time Series Analysis

Background:

Exchange rate forecasting is challenging due to non-linearity and non-stationarity.
Existing models struggle with complex temporal dynamics in currency markets.

Purpose of the Study:

Introduce AFD-Net, a novel deep learning framework for enhanced exchange rate prediction.
Synergistically integrate frequency-domain decomposition, attention mechanisms, and linear time series modeling.

Main Methods:

Employ Feature-wise Adaptive Normalization (FAN) for frequency decomposition and stationarity.
Utilize an attention module to capture salient temporal dependencies.
Leverage a DLinear backbone for efficient trend and seasonal component prediction.

Main Results:

AFD-Net consistently outperforms seven state-of-the-art models on Exchange-Rate and IMF datasets.
Demonstrates superior performance in long-term trend modeling.
Ablation studies confirm the contributions of FAN and attention components.

Conclusions:

AFD-Net offers enhanced accuracy and robustness in financial time series forecasting.
Frequency-aware architectures show significant potential for improving prediction models.
The FFT-based design of FAN provides substantial performance gains with manageable computational overhead.